Natural Language Processing

Python R LLM Polars Natural Language Processing Tabular Data

Introducing mall for R...and Python

We are proud to introduce the {mall} package. With {mall}, you can use a local LLM to run NLP operations across a data frame. (sentiment, summarization, translation, etc). {mall} has been simultaneously released to CRAN and PyPi (as an extension to Polars)

Edgar Ruiz

Oct 30, 2024

What are Large Language Models? What are they not?

What are Large Language Models? What are they not?

This is a high-level, introductory article about Large Language Models (LLMs), the core technology that enables the much-en-vogue chatbots as well as other Natural Language Processing (NLP) applications. It is directed at a general audience, possibly with some technical and/or scientific background, but no knowledge is assumed of either deep learning or NLP. Having looked at major model ingredients, training workflow, and mechanics of output generation, we also talk about what these models are not

Meta Concepts Natural Language Processing

Jun 20, 2023

GPT-2 from scratch with torch

Implementing a language model from scratch is, arguably, the best way to develop an accurate idea of how its engine works. Here, we use torch to code GPT-2, the immediate successor to the original GPT. In the end, you'll dispose of an R-native model that can make direct use of Hugging Face's pre-trained GPT-2 model weights

Torch R Natural Language Processing

Jun 20, 2023

TensorFlow/Keras R Generative Models Natural Language Processing

LLaMA in R with Keras and TensorFlow

Implementation and walk-through of LLaMA, a Large Language Model, in R, with TensorFlow and Keras

Tomasz Kalinowski

May 25, 2023

Oscar Kjell, Salvatore Giorgi, H Andrew Schwartz

Introducing the text package

The text package attempts to provide user-friendly access and pipelines to HuggingFace's transformer language models in R

Oct 4, 2022

Natural Language Processing R

Natural Language Processing

State-of-the-art NLP models from R

Nowadays, Microsoft, Google, Facebook, and OpenAI are sharing lots of state-of-the-art models in the field of Natural Language Processing. However, fewer materials exist how to use these models from R. In this post, we will show how R users can access and benefit from these models as well

Turgut Abdullayev

Jul 30, 2020

Sigrid Keydana, Javier Luraschi

Innocent unicorns considered harmful? How to experiment with GPT-2 from R

Innocent unicorns considered harmful? How to experiment with GPT-2 from R

Is society ready to deal with challenges brought about by artificially-generated information - fake images, fake videos, fake text? While this post won't answer that question, it should help form an opinion on the threat exerted by fake text as of this writing, autumn 2019. We introduce gpt2, an R package that wraps OpenAI's public implementation of GPT-2, the language model that early this year surprised the NLP community with the unprecedented quality of its creations

Oct 23, 2019

Natural Language Processing Packages/Releases

Natural Language Processing TensorFlow/Keras

BERT from R

A deep learning model - BERT from Google AI Research - has yielded state-of-the-art results in a wide variety of Natural Language Processing (NLP) tasks. In this tutorial, we will show how to load and train the BERT model from R, using Keras

Turgut Abdullayev

Sep 30, 2019

Attention-based Image Captioning with Keras

Attention-based Image Captioning with Keras

Image captioning is a challenging task at intersection of vision and language. Here, we demonstrate using Keras and eager execution to incorporate an attention mechanism that allows the network to concentrate on image features relevant to the current state of text generation

Natural Language Processing TensorFlow/Keras Image Recognition & Image Processing

Sep 17, 2018

Attention-based Neural Machine Translation with Keras

Attention-based Neural Machine Translation with Keras

As sequence to sequence prediction tasks get more involved, attention mechanisms have proven helpful. A prominent example is neural machine translation. Following a recent Google Colaboratory notebook, we show how to implement attention in R

Natural Language Processing TensorFlow/Keras

Jul 30, 2018

Analyzing rtweet Data with kerasformula

The kerasformula package offers a high-level interface for the R interface to Keras. It’s main interface is the kms function, a regression-style interface to keras_model_sequential that uses formulas and sparse matrices. We use kerasformula to predict how popular tweets will be based on how often the tweet was retweeted and favorited

Pete Mohanty

Jan 24, 2018

Classifying Duplicate Questions from Quora with Keras

Classifying Duplicate Questions from Quora with Keras

In this post we will use Keras to classify duplicated questions from Quora. Our implementation is inspired by the Siamese Recurrent Architecture, with modifications to the similarity measure and the embedding layers (the original paper uses pre-trained word vectors)

Daniel Falbel

Jan 9, 2018

Word Embeddings with Keras

Word embedding is a method used to map words of a vocabulary to dense vectors of real numbers where semantically similar words are mapped to nearby points. In this example we'll use Keras to generate word embeddings for the Amazon Fine Foods Reviews dataset

Daniel Falbel

Dec 22, 2017

François Chollet, J.J. Allaire

Deep Learning for Text Classification with Keras

Deep Learning for Text Classification with Keras

Two-class classification, or binary classification, may be the most widely applied kind of machine-learning problem. In this excerpt from the book Deep Learning with R, you'll learn to classify movie reviews as positive or negative, based on the text content of the reviews

Dec 7, 2017